Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentii-ai/agentii-investment-intelligence --skill competitivegit clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligenceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/competitive)<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/competitive"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/competitive/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/competitive"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/competitive.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.01731 |
| Opus 5 | $0.00020 | $0.00865 |
| Sonnet 5 | $0.00008 | $0.00346 |
| Haiku 4.5 | $0.00004 | $0.00173 |
Grade A, and why
competitive scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preflight
Run the canonical pre-flight sequence — MCP health probe, ticker resolution, workspace style.md override, memory load, and coverage check. See contracts/preflight.md.
Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md.
Triggers
- analyze dim competitive landscape
- run dim competitive landscape analysis
- produce dim competitive landscape report
- dim competitive landscape breakdown
- dim competitive landscape deep dive
- build a dim competitive landscape
- assess dim competitive landscape
- quantify dim competitive landscape
- compare dim competitive landscape across peers
- review dim competitive landscape for
- generate dim competitive landscape on
- dim competitive landscape for investment decision
Defaults
| Parameter | Default | Notes |
|---|---|---|
| lookback_years | 3 | Historical data window |
| include_peers | false | Whether to surface a peer comparison block |
Methodology
Retrieval Scope
This skill performs unstructured document search at scale (10-K, 10-Q, 8-K filings and earnings call transcripts spanning multiple fiscal periods). The three-layer agent-use-ready retrieval protocol (Document Discovery → Page Map → Deep Read) applies to all unstructured document search at scale.
Retrieval Strategy
Follow the retrieval strategy decision tree in contracts/retrieval.md. This skill uses:
- Branch (a) for structured financial metrics via
search_xbrl_factswithlist_xbrl_conceptspre-condition for unfamiliar concepts. - Branch (c) for single-period document queries via direct
read_source_outline→read_source_pages. - Branch (d) for simple lookups via
get_company_profile/search_earnings_calendar.
**Layer 1 secondary_label allowlist **: prefer ?secondary_labels=material_definitive_agreement_1_01,other_events_8_01 to surface M&A / partnership / strategic-action 8-Ks before Layer 2.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +1 lines 7001427b8953
- 7d ago Changed · +7 lines d66ea9060818
- 13d ago First seen · 130 lines · 40 tokens per session scan A fdbff7a5b12e
competitive is a skill published in the GitHub repository agentii-ai/agentii-investment-intelligence (204 stars, last pushed 2d ago), licensed Apache-2.0. It adds 40 tokens to every session and 1,731 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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